Update app.py

app.py

@@ -1,42 +1,42 @@
-   import gradio as gr
-   from transformers import pipeline
-   
-   # 加载模型
-   print("正在加载病理检测NER模型...")
-   ner = pipeline(
-       "token-classification", 
-       model="OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M",
-       aggregation_strategy="max"
-   )
-   print("模型加载完成!")
-   
-   # 处理函数
-   def process_text(text):
-       if not text:
-           return "请输入医学文本"
-           
-       results = ner(text)
-       output = ""
-       
-       for result in results:
-           entity = result["entity_group"]
-           word = result["word"]
-           score = round(result["score"], 2)
-           output += f"检测到病理实体: {word} (类型: {entity}, 置信度: {score})\n"
-           
-       if not output:
-           output = "未检测到任何病理相关实体"
-           
-       return output
-   
-   # 创建界面
-   demo = gr.Interface(
-       fn=process_text,
-       inputs=gr.Textbox(placeholder="请输入医学文本...", lines=5),
-       outputs="text",
-       title="OpenMed 病理检测 NER 模型演示",
-       description="使用OpenMed-NER-PathologyDetect-BigMed-560M模型识别文本中的病理实体"
-   )
-   
-   # 启动服务
-   demo.launch()
+import gradio as gr
+from transformers import pipeline
+
+# 加载模型
+print("正在加载病理检测NER模型...")
+ner = pipeline(
+    "token-classification", 
+    model="OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M",
+    aggregation_strategy="max"
+)
+print("模型加载完成!")
+
+# 处理函数
+def process_text(text):
+    if not text:
+        return "请输入医学文本"
+        
+    results = ner(text)
+    output = ""
+    
+    for result in results:
+        entity = result["entity_group"]
+        word = result["word"]
+        score = round(result["score"], 2)
+        output += f"检测到病理实体: {word} (类型: {entity}, 置信度: {score})\n"
+        
+    if not output:
+        output = "未检测到任何病理相关实体"
+        
+    return output
+
+# 创建界面
+demo = gr.Interface(
+    fn=process_text,
+    inputs=gr.Textbox(placeholder="请输入医学文本...", lines=5),
+    outputs="text",
+    title="OpenMed 病理检测 NER 模型演示",
+    description="使用OpenMed-NER-PathologyDetect-BigMed-560M模型识别文本中的病理实体"
+)
+
+# 启动服务
+demo.launch()